Target-Value-Competition-Based Multi-Agent Deep Reinforcement Learning Algorithm for Distributed Nonconvex Economic Dispatch

Ding, Lifu; Lin, Zhiyun; Shi, Xiasheng; Yan, Gangfeng

doi:10.1109/tpwrs.2022.3159825

Cited by 19 publications

(3 citation statements)

References 31 publications

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“…This method tackled issues like overfitting and high model variance that plagued DDPG. Additionally, Ding et al [114] delved into distributed non-convex economic dispatch using a novel target value competition-MADRL-TVC approach. Their method allowed each power generation unit in the smart grid to run an independent DRL algorithm, although the discretization process can cause a slight deviation in the balance of the recommended action and demand.…”

Section: F Power Flow Control and Grid Restorationmentioning

confidence: 99%

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Massaoudi,

Abu-Rub,

Ghrayeb

2023

IEEE Access

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The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) breakthroughs have been in the spotlight over the last few years with potential contributions to PS stability (PSS). The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. Furthermore, this paper highlights the theoretical advantages and the key tradeoffs of the emerging DRL techniques as powerful tools for optimal power flow. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in large-scale PSS is also presented. This review aims to support research in this area of DRL algorithms to embrace PSS against unseen faults and different PS topologies.INDEX TERMS Deep reinforcement learning, dynamic security control, electric power systems, power system stability, smart grids. Nomenclature

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Section: F Power Flow Control and Grid Restorationmentioning

confidence: 99%

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Massaoudi,

Abu-Rub,

Ghrayeb

2023

IEEE Access

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“…The fundamental difference compared to [39] is that the agent in [40] uses not only the local measurements of electrical quantities for the state information, but also messages from the neighbouring agents, leading to improved performance. Work [41] proposes using a MADRL algorithm to perform the economic dispatch, which minimizes the overall cost of generation while satisfying the power demand. The agent models an individual power plant in a power system, with the action being the active power production set point.…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Ognjen¹,

Forcan

Cosovic

et al. 2022

2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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Electric power systems consist of generation, distribution, and transmission systems, which are all traditionally coordinated from the corresponding control centres. System operators use specialized software solutions for monitoring and optimization of electric power systems, installed in control centres. Typical algorithms implemented in mentioned software solutions should satisfy near real-time operation requirements, while delivering accurate information for power system monitoring and optimizing its operation.Modern electric power systems have been increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the topic of this dissertation is the application of deep learning algorithms, namely graph neural networks (GNNs) and deep reinforcement learning (DRL), for monitoring and optimization of electric power systems.

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“…Deep reinforcement learning based applications in power system are spreading widely because of its capability in solving challenges like decision making in complex and dynamic environment. Recent studies have demonstrated the effective use of DRL-based techniques in resolving various power system issues with satisfactory results, including grid operation [1,2], grid emergency control [3][4][5], energy trading [6][7][8], electricity markets [9], battery control [10,11], demand response [12], economic dispatch [13], cyber security [14][15][16], load-frequency control [17], and real-time topology control [18]. Some of the advantages of the DRL method over traditional methods include flexibility, continuous learning, and the elimination of the need for explicit models.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based voltage control revisited

Nematshahi,

Shi,

Wang

et al. 2023

IET Generation Trans & Dist

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Deep Reinforcement Learning (DRL) has shown promise for voltage control in power systems due to its speed and model‐free nature. However, learning optimal control policies through trial and error on a real grid is infeasible due to the mission‐critical nature of power systems. Instead, DRL agents are typically trained on a simulator, which may not accurately represent the real grid. This discrepancy can lead to suboptimal control policies and raises concerns for power system operators. In this paper, we revisit the problem of RL‐based voltage control and investigate how model inaccuracies affect the performance of the DRL agent. Extensive numerical experiments are conducted to quantify the impact of model inaccuracies on learning outcomes. Specifically, techniques that enable the DRL agent are focused on learning robust policies that can still perform well in the presence of model errors. Furthermore, the impact of the agent's decisions on the overall system loss are analyzed to provide additional insight into the control problem. This work aims to address the concerns of power system operators and make DRL‐based voltage control more practical and reliable.

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Target-Value-Competition-Based Multi-Agent Deep Reinforcement Learning Algorithm for Distributed Nonconvex Economic Dispatch

Cited by 19 publications

References 31 publications

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Deep reinforcement learning based voltage control revisited

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